The present invention relates to the field of computerized data mining and particularly to searching for critical mass solutions in data.
An example of a data mining problem might be selecting a location for a branch of an existing business, or a location for a new business, based upon the past failures and successes of similar businesses in similar locations. For example, in the retail industry, it is helpful to construct a set of rules that determine where a new retail store should be placed. Mining data for the most frequent patterns associated with a set of successful retail stores (common features with high likelihood) is a useful method of discovering such rules from historical data.
One form of data mining ignores quantitative information, and converts the data into Boolean patterns, see e.g. Jiawei Han, Jian Pei, Yiwen Yin and Runying Mao, “Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach”, Data Mining and Knowledge Discovery, Volume 8, pp. 53-87, 2004. In a Boolean pattern, either a feature is ‘present’ or ‘not present’ within the pattern. For example, a retail store may be successful in neighborhoods that have an office building and a supermarket. These features of the Boolean pattern, ‘office building’ and ‘supermarket’, are either present or not present within the neighborhood. If these features are present, then it is an indication that a new retail store may perform well at a location within the neighborhood.
However mining Boolean data patterns does not solve a more important problem in selecting a location for a retail store. This problem is known as the critical mass problem. The critical mass problem addresses the minimum quantity of each feature a location would have in order for a new retail store to be successful at that location. Another type of search is called “exhaustive search.” This type of search looks for patterns one by one and is considered slow in comparison with other types of search algorithms.